The most commonly used features in unsupervised change detection are spectral characteristics. Traditional methods describe the degree of the change between two pixels by quantifying the difference in spectral values or spectral shapes (spectral curv...
This paper analyzes the current state of water quality detection equipment and, based on the demand for portable water quality detection systems that are on-site, rapid, accurate, cost-effective, and capable of multi-parameter measurements using spec...
Hyperspectral anomaly detection is used to recognize unusual patterns or anomalies in hyperspectral data. Currently, many spectral–spatial detection methods have been proposed with a cascaded manner; however, they often neglect the complementar...
On-chip multi-dimensional detection systems integrating pixelated polarization and spectral filter arrays are the latest trend in optical detection instruments, showing broad application potential for diagnostic medical imaging and remote sensing. Ho...
Audio deepfake detection systems often achieve excellent internal validation performance but fail to generalize under real-world inference conditions involving synthetic speech generated with previously unseen AI tools. To address this limitation, th...
Spectral detection technology offers non-destructive, in situ, and high-speed capabilities, making it widely applicable for detecting biological and chemical samples and quantifying their concentrations. Water resources, essential to life on Earth, a...
Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the...
Early detection of machine failures is often beneficial, both financially and in terms of worker safety. The article presents the problem of frequently damaged joints in haul trucks, which are a real threat to the health and life of drivers. It was d...
Deep-Learning-based (DL-based) approaches have achieved remarkable performance in hyperspectral image (HSI) change detection (CD). Convolutional Neural Networks (CNNs) are often employed to capture fine spatial features, but they do not effectively e...
Hyperspectral sensors provide high spectral resolution, enabling accurate material discrimination and effective target detection. However, their practical use is constrained by limited spatial resolution and high acquisition costs. This paper propose...
Hyperspectral object-detection algorithms based on deep learning have been receiving increasing attention due to their ability to operate without relying on prior spectral information about the target and their strong real-time inference performance....
The infrared spectral radiance of O2 is of great significance for space-based infrared detection. In this work, based on the demand for near-infrared spectral radiance of O2 limb detection, a method of spectral radiance calculation coupled with an at...
Cloud detection is an important procedure for the processing of remote sensing images. A cloud detection scheme driven by the spectral and the temporal features is presented in this paper, where an unsupervised hierarchy clustering approach is propos...
This work aims to develop approaches to processing and interpreting spectral entropy outcomes in the context of seismic data, as well as to establish a methodological foundation for subsequent integration into practical monitoring solutions. The obje...
In this paper, we report on the spectral detection of wustite, Fe(II) oxide (FeO), and magnetite, Fe(II, III) oxide (Fe3O4), molecular emissions during the combustion of pyrite (FeS2), in a laboratory-scale furnace operating at high temperatures. The...
In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty...
Generalized tonic-clonic seizures (GTCSs) can be underestimated and can also increase mortality rates. The monitoring devices used to detect GTCS events in daily life are very helpful for early intervention and precise estimation of seizure events. S...
Target detection in complex backgrounds remains challenging because targets and backgrounds are often insufficiently separable in a single spectral or polarimetric feature space. In addition, conventional detectors usually have limited capability to...
Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researc...
Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based o...
The accurate onboard detection of clouds in hyperspectral images before lossless compression is beneficial. However, conventional onboard cloud detection methods are not applicable all the time, especially for shadowed clouds or darkened snow-covered...
Monitoring the plant’s health and early detection of disease are essential to facilitate effective management, decrease disease spread, and minimize yield loss. Spectroscopic techniques in remote sensing offer less laborious methods and high sp...
Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to...
The spectral information extracted from hyperspectral images is characterized by redundancy and complexity, while the spectral morphological features extracted from the spectral information help to simplify the data and provide rich information about...
Conventional wavefront sensors face challenges when detecting frequency-domain information. In this study, we developed a high-precision, and fast multi-spectral wavefront detection technique based on neural networks. Using an etalon and a diffractiv...
Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the qua...
Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-le...
Modern methods of detection and identification of structural damage direct the activities of scientific groups towards the improvement of diagnostic methods using for example the phenomenon of mechanical wave propagation. Damage detection methods tha...
Marine oil spills are one of the most serious problems of marine environmental pollution. Hyperspectral remote sensing has been proven to be an effective tool for monitoring marine oil spills. To make full use of spectral and spatial features, this s...
Large-area, artificial floating marine litter (FML) targets were deployed during a controlled field experiment and data acquisition campaign: the Plastic Litter Project 2021. A set of 22 Sentinel-2 images, along with UAS data and ancillary measuremen...
The need for affordable low-power devices has led MEMS-based thermal emitters to become an interesting option for optical gas sensors. Since these emitters have a low thermal mass, they can be easily modulated and combined with a lock-in amplifier fo...
Remote sensing (RS) object detection faces intrinsic challenges arising from the overhead imaging paradigm and the diversity of climatic conditions. In particular, atmospheric phenomena such as clouds and haze cause severe visual degradation, making...
Outlier detection is an essential research field in data mining, especially in the areas of network security, credit card fraud detection, industrial flaw detection, etc. The existing outlier detection algorithms, which can be divided into supervised...
Under low-light conditions, visible-spectrum images are prone to detail loss and contrast degradation, which substantially limits object detection performance. Although infrared imagery can provide complementary cues, direct fusion often introduces n...
To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual inf...
Accurate cloud detection remains a significant challenge due to the spectral ambiguity between clouds and bright or heterogeneous surfaces (e.g., snow, desert). While multi-angle and polarization data offer rich information, the discriminative power...
Traditional facial recognition is realized by facial recognition algorithms based on 2D or 3D digital images and has been well developed and has found wide applications in areas related to identification verification. In this work, we propose a novel...
Nowadays, pedestrian detection is widely used in fields such as driving assistance and video surveillance with the progression of technology. However, although the research of single-modal visible pedestrian detection has been very mature, it is stil...
A spectral integration combined with a threshold method for the analysis of spectral scanning surface plasmon resonance (SPR) images can significantly increase signal recognition at low concentration of antibody solution. The 12-well SPR sensing plat...
The aim of this study is to enhance the detection accuracy of rice chlorophyll content under complex backgrounds, optimize rice production management models, and improve the efficiency and quality of grain production. To achieve this, a spectral devi...
Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spec...
Most existing hyperspectral anomaly detection algorithms primarily rely on spatial information to identify anomalous targets. However, they often overlook the spatial–spectral gradient information inherent in hyperspectral images, which can lea...
Hyperspectral Imagery (HSI) plays a crucial role in military and civilian target detection. However, HSI target detection remains highly challenging due to the interference caused by complex and diverse real-world scenarios. This paper proposes a Spe...
In the research of fault arc detection technology, proper signal denoising can greatly improve the recognition ability of the arc fault detection algorithm. Therefore, this study proposes a current signal enhancement algorithm based on improved spect...
Hyperspectral target detection identifies target pixels in complex backgrounds using a small number of known target priors; this task is complicated by target-sample scarcity, pseudo-sample contamination, and spectral similarity between targets and b...
Classical coherent chaos shift keying (CSK) provides certain physical-layer advantages by exploiting chaotic waveforms, but it relies on fragile chaos synchronization, which is difficult to maintain in multipath fading channels. To overcome this limi...
Due to the limited penetration of visible bands, optical remote sensing images are inevitably contaminated by clouds. Therefore, cloud detection or cloud mask products for optical image processing is a very important step. Compared with conventional...
Hyperspectral remote sensing image (HSI) include rich spectral information that can be very beneficial for change detection (CD) technology. Due to the existence of many mixed pixels, pixel-wise approaches can lead to considerable errors in the resul...
Anomaly detection is a crucial task for hyperspectral image processing. Most popular methods detect anomalies at the pixel level, while a few algorithms for anomaly detection only utilize subpixel level unmixing technology to extract features without...
Change detection (CD) in high-resolution remote sensing imagery is vital for resource monitoring and disaster assessment but faces challenges such as spatiotemporal heterogeneity, spectral variability, and computational inefficiency. This paper propo...
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